Miniaturization of electronics has led to the development of many devices that can mediate human-computer interactions, and which are finding their way in to day-to-day consumer products. In particular, computer systems these days can employ a variety of relatively inexpensive, and often unintrusive, sensors that measure users' physiological and/or behavioral signals. For example, cameras and microphones are able to track user's gestures, facial expressions, and voice. In addition, there are various types of physiological sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG). These sensors come in many forms, and can be attached to, or embedded in, devices, clothing, and even implanted in the human body. Information collected by such sensors paves the way to affective computing applications, in which computer systems can deduce how users feel and gain insight towards the emotional responses the users express. The knowledge about a user's emotional response may be utilized by the affective computing applications in order to deliver content that is more relevant to the user and/or to tailor services to improve the user's experience.
Wide-spread adoption of computational platforms such as mobile devices has made it possible for user to communicate with large-scale networks such as the internet practically anytime and anyplace they choose. These platforms also give users the freedom to utilize services from a plethora of remote computational systems such as cloud-based computing applications. Consequently, users are exposed to large amounts of digital content many times a day, and for long periods of time. Some examples of content users are likely to be exposed to include various forms of digital media (e.g., internet sites, television shows, movies, and/or interactive computer games), communications with other users (e.g., video conversations and instant messages), and/or communications with a computer (e.g., interaction with a user's virtual agent). Throughout these many interactions, it may be useful for computer systems running applications to measure a user's affective response to the content, in order to improve the selection and/or delivery of content to the user.
Since users may practically be interacting with computer systems all day long, this may result in users having their affective response measured for long periods; consequently, copious amounts of data may be generated. This raises several issues concerning the computational resources a computer system may have to consume in order to process and/or transmit the collected data. For example, sensors like cameras or EEG sensors often produce many high dimensional data points. Processing this data, such as filtering, analyzing, extracting features, compressing, and/or encrypting can require a system to perform a significant amount of computations. In addition, sensor measurement data is often collected by mobile battery powered devices, and processing and/or transmitting the data is also performed, at least in part, on mobile devices. Therefore, handling the affective response measurement data can involve an expenditure of energy from the mobile devices' limited energy supplies.
Thus, there is a need to reduce the computational load involved in processing data that includes measurements of users' affective response signals. Unchecked expenditure of computational resources may deplete system resources and/or reduce the systems' ability to operate effectively when needed; this can ultimately end up reducing the quality of information and/or services the computer systems are able to provide their users.